On Computation of Degree-Based Entropy of Planar Octahedron Networks

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Introduction
All the graphs in this article are finite and undirected. A graph is set of points, where each pair of points (also known as vertex) are connected by an edge (also known as link or line). In network, vertices are called nodes, and in chemical graph, vertices are called atoms. In network, edges are called links or lines, while in chemical graph, they are called covalent bonds. The subbranch of chemical graph theory is topological indices. Many articles have been written on the topic of topological index. The representation of molecular graph by a drawing, a polynomial, a sequence of numbers, a matrix, or a derived number is called a topological index. As such, under graph isomorphism, these numeric numbers are unique. Most of the time, molecules and molecular compounds are nicely presented by molecular graph for better understanding.
Topological descriptors assume fundamental job in QSAR/QSPR studies in light of the fact that they convert a compound graph into a numerical number. We compare other physicochemical properties of carbon-based compounds (such as nanotubes, hydrocarbons, nanocones, and fullerenes). Due to these properties, topological descriptors have many applications in organic chemistry, biotechnology, and nanotechnology.
Cheminformatics is a branch of science that participates in mathematics, chemistry, and IT. In chemical graph theory, we consider molecular graph's solution using the graph theory techniques which is the subdivision of mathematical chemistry. Molecules or atoms are represented by vertices in chemical graph theory, also the bonds between them by edges [1].
The pioneer of topological indices is Wiener [2]. It is defined as Randić presented first the vertex-degree-based topological index in 1975 [3], which is written by Bollobás and Erdos [4] and Amić et al. [5] compute the "general Randić index" independently in 1998.
ABC index was introduced in 1998, by Estrada et al. [6]. It has the formulae Vukicević and Furtula were the persons who studied this index for the first time [7]. It is written as GA index and written as Entropy is the uncertainty in a random variable or quantity. In other words, it is the information obtained by learning the values of some unknown variables. Entropy has many applications in information theory as information entropy, in chemistry as thermodynamic entropy, and in graph theory as graph entropy [8][9][10][11][12][13][14][15][16]. In general, entropy is defined as the following: Let x be a discrete random variable and x ∈ X and p be the probability distribution of set X. Then, entropy of x is The definition of entropy was given by Shannon in 1948 [17]. In graph theory, the idea of graph entropy was given by Rashevsky in 1955 [18]. It has been used comprehensively to depict the design of graph-based systems in mathematical science [19]. The graph entropy is defined as the following: For a graph G, V ðGÞ is finite vertex set. Let P be the density of probability of vertex set and V P ðGÞ be the vertex packing polytope of G. Then, entropy of G with respect to P is Octahedron networks have its roots in physical world as natural crystals of diamond are octahedron; also, many metal ions have octahedron configuration. In physics, these networks can be used as circuits. The construction of planar octahedron network POH is based on silicate structure derived by Manuel and Rajasingh [20] and POH was derived by Simonraj and George [21] (for the complete construction of POH, see Figure 1, for triangular prism network TP, see Figure 2, and for hex planar octahedron network, see Figure 3; we refer the reader to read the article [22]).
Degree-based entropy is defined as From Equation (8), edge-based entropy can be deducted as From Equation (3) and Equation (9), Randić entropy will be From Equation (4) and Equation (9), ABC entropy will be

Journal of Function Spaces
From Equation (5) and Equation (9), GA entropy will be

Main Results
Planar octahedron network and its derived forms are inorganic structures used in chemistry. Here, we research some degree-based entropies for these networks. These days, there is a broad examination movement on entropies (for further studies, see [23,24]; for basic definitions and notations, we refer the reader to [25,26]).

Results on Planar Octahedron Network.
In this section, we will compute Randić, ABC, and GA entropies for planar octahedron network. The edge partition of POHðnÞ is written in Table 1.

GA Entropy.
If G 1 ≅ POHðnÞ, then from Table 1 and Equation (5), we have Using Equation (12) and Table 1, we have where GA index is written in (26).

Results on Triangular Prism Network.
In this section, we will compute Randić, ABC, and GA entropies for triangular prism network. The edge partition of TPðnÞ is written in Table 2.

GA Entropy.
If G 2 ≅ TPðnÞ, then from Table 2 Using Equation (12) and Table 2, we have where GA index is written in (39).

Results on Hex Planar Octahedron Network.
In this section, we will compute Randić, ABC, and GA entropies for hex planar octahedron network. The edge partition of hex POHðnÞ is written in Table 3.